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COMPUTATIONAL MODELING OF HUMAN INTERACTION BEHAVIOR
TOWARDS CLINICAL TRANSLATION IN AUTISM SPECTRUM DISORDER
by
Daniel Bone
A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Ful llment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(ELECTRICAL ENGINEERING)
August 2016
Copyright 2016 Daniel Bone

This thesis concerns human-centered signal processing and machine learning, with a focus on creating engineering techniques and systems for societal applications in human health and well-being. Specifically, I aim to develop novel computational methods and tools that will support clinicians and researchers in the domain of autism spectrum disorder (ASD)—ASD has a population prevalence of 1 in 68 (Baio, 2014). Computational methods of behavioral characterization can augment the clinician’s analytical capabilities in diagnosis, personalized intervention, and long-term monitoring. Computational dimensional descriptors of behavior may be integral to further developments in the biology and neurology of ASD; they offer a scalable quantitative framework to address a scientific and translational need, augmenting current qualitative methods. ❧ A primary target for computational modeling is speech prosody—the rhythm and timing of speech that communicates meaning and affect—which is difficult to precisely specify qualitatively, even for experts. Children with ASD are almost universally delayed in language acquisition, and early language ability is a primary indicator of long-term prognosis. Prosody is a key marker for early diagnosis, but is still not reliably evaluated. My work aims to automatically characterize social prosody from spontaneous speech samples using statistical signal processing. ❧ Moreover, social prosody does not occur in isolation, but during interaction with a communicative partner. This thesis illustrates the need to model both sides of the interaction simultaneously. My research has produced original and important findings about human interaction in ASD, findings which have translational potential. In particular, I have found that the prosodic, turn-taking, and language cues of the psychologist alter during ASD assessment depending on the level of social-communicative impairment the child displays. In other words, the psychologist’s behavior is predictive of the child’s level of impairment. Such findings may eventually influence design of novel psychometric instruments which focus on attunements in the psychologist’s behavior in order to assess the child’s behavior, or be used in the evaluation of new intervention strategies. ❧ Towards the goal of automatic systems, I have created a robust affective measure from speech in computational vocal arousal. This rule-based method is shown to be robust across databases, and competes well with the state-of-the-art in supervised approaches. I have utilized this method to automatically label vocal arousal in child-psychologist interactions. Next, I investigated the synchrony between child and psychologist, finding interesting results; for instance, for sessions with children having low ASD severity, the psychologist leads the affective exchange, but for sessions with high ASD severity, the child becomes less responsive and leads on average. ❧ Additionally, having approached these problems through the lens of BSP, I have discovered certain pitfalls that must be avoided. Through dissemination in peer-reviewed articles, this thesis also contributes to the amassing set of standards of practice in Behavioral Signal Processing (BSP). ❧ Lastly, I have investigated application of machine learning to autism diagnostics and screening. I have shown that sensitivity and specificity can be readily tuned to optimal level through careful application of machine learning to this new domain. I have also shown that instruments can be fused to improve performance. The primary outcome is a screener algorithm which achieves 95% of the instruments performance using only 5% of the available codes.

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COMPUTATIONAL MODELING OF HUMAN INTERACTION BEHAVIOR
TOWARDS CLINICAL TRANSLATION IN AUTISM SPECTRUM DISORDER
by
Daniel Bone
A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Ful llment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(ELECTRICAL ENGINEERING)
August 2016
Copyright 2016 Daniel Bone